High-Accuracy Polynomial Model Training for RF Power Amplifiers: Leveraging FISTA Unrolling

Abstract

Power amplifier (PA) behavioral modeling, incorporating nonlinearity and memory effects, provides the basis for predistorters and accurate base station models for analyzing cellular network coverage. In this letter, we present two fast iterative shrinkage and thresholding algorithm (FISTA) approaches to PA behavioral modeling: an FISTA-based and a learnable FISTA-based method for parameter estimation of a polynomial model. FISTA addresses sparsity-regularized linear inverse problems with low complexity and fast convergence, enabling the efficient feature selection in PA modeling. Both approaches are experimentally validated using a 100-MHz bandwidth signal. The proposed coefficient extraction techniques demonstrate superior accuracy and speed while maintaining the computational efficiency compared with commonly used models like the memory polynomial (MP), generalized MP (GMP), and popular neural network (NN) models.